基于小波的无监督标签到图像转换
Wavelet-based Unsupervised Label-to-Image Translation
AI总结：
提出基于小波的无监督语义图像合成范式USIS，利用自监督分割损失和整图小波判别及新型小波域生成器架构，在3个数据集上有效弥合了配对与无配对模型间的性能差距。
摘要：
语义图像合成（SIS）是图像到图像转换的一个子类，它使用语义布局来生成逼真的图像。最先进的条件生成对抗网络（GAN）需要大量配对数据来完成此任务，而通用的无配对图像到图像转换框架则表现不佳，因为它们对语义布局进行颜色编码，并学习外观的对应关系而非语义内容。从高质量生成图像应能被分割回其语义布局的假设出发，我们提出了一种用于SIS的新无监督范式（USIS），该范式利用自监督分割损失和基于整幅图像的小波判别。此外，为了匹配真实图像的高频分布，提出了一种新的小波域生成器架构。我们在3个具有挑战性的数据集上测试了我们的方法，并证明了其弥合配对和无配对模型之间性能差距的能力。
英文摘要：
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge amount of paired data to accomplish this task while generic unpaired image-to-image translation frameworks underperform in comparison, because they color-code semantic layouts and learn correspondences in appearance instead of semantic content. Starting from the assumption that a high quality generated image should be segmented back to its semantic layout, we propose a new Unsupervised paradigm for SIS (USIS) that makes use of a self-supervised segmentation loss and whole image wavelet based discrimination. Furthermore, in order to match the high-frequency distribution of real images, a novel generator architecture in the wavelet domain is proposed. We test our methodology on 3 challenging datasets and demonstrate its ability to bridge the performance gap between paired and unpaired models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
